Overview

Dataset statistics

Number of variables22
Number of observations940
Missing cells6580
Missing cells (%)31.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory201.2 KiB
Average record size in memory219.2 B

Variable types

Numeric14
Categorical1
Unsupported7

Alerts

TotalSteps is highly overall correlated with TotalDistance and 8 other fieldsHigh correlation
TotalDistance is highly overall correlated with TotalSteps and 8 other fieldsHigh correlation
TrackerDistance is highly overall correlated with TotalSteps and 8 other fieldsHigh correlation
VeryActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
ModeratelyActiveDistance is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightActiveDistance is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
VeryActiveMinutes is highly overall correlated with TotalSteps and 6 other fieldsHigh correlation
FairlyActiveMinutes is highly overall correlated with TotalSteps and 5 other fieldsHigh correlation
LightlyActiveMinutes is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
Calories is highly overall correlated with TotalSteps and 3 other fieldsHigh correlation
Date has 940 (100.0%) missing valuesMissing
WeightKg has 940 (100.0%) missing valuesMissing
WeightPounds has 940 (100.0%) missing valuesMissing
Fat has 940 (100.0%) missing valuesMissing
BMI has 940 (100.0%) missing valuesMissing
IsManualReport has 940 (100.0%) missing valuesMissing
LogId has 940 (100.0%) missing valuesMissing
ActivityDate is uniformly distributedUniform
Date is an unsupported type, check if it needs cleaning or further analysisUnsupported
WeightKg is an unsupported type, check if it needs cleaning or further analysisUnsupported
WeightPounds is an unsupported type, check if it needs cleaning or further analysisUnsupported
Fat is an unsupported type, check if it needs cleaning or further analysisUnsupported
BMI is an unsupported type, check if it needs cleaning or further analysisUnsupported
IsManualReport is an unsupported type, check if it needs cleaning or further analysisUnsupported
LogId is an unsupported type, check if it needs cleaning or further analysisUnsupported
TotalSteps has 77 (8.2%) zerosZeros
TotalDistance has 78 (8.3%) zerosZeros
TrackerDistance has 78 (8.3%) zerosZeros
LoggedActivitiesDistance has 908 (96.6%) zerosZeros
VeryActiveDistance has 413 (43.9%) zerosZeros
ModeratelyActiveDistance has 386 (41.1%) zerosZeros
LightActiveDistance has 85 (9.0%) zerosZeros
SedentaryActiveDistance has 858 (91.3%) zerosZeros
VeryActiveMinutes has 409 (43.5%) zerosZeros
FairlyActiveMinutes has 384 (40.9%) zerosZeros
LightlyActiveMinutes has 84 (8.9%) zerosZeros

Reproduction

Analysis started2023-01-21 22:25:00.321395
Analysis finished2023-01-21 22:25:14.983704
Duration14.66 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

Id
Real number (ℝ)

Distinct33
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8554074 × 109
Minimum1.5039604 × 109
Maximum8.8776894 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.033964image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.5039604 × 109
5-th percentile1.6245801 × 109
Q12.320127 × 109
median4.445115 × 109
Q36.9621811 × 109
95-th percentile8.7920097 × 109
Maximum8.8776894 × 109
Range7.373729 × 109
Interquartile range (IQR)4.6420541 × 109

Descriptive statistics

Standard deviation2.4248055 × 109
Coefficient of variation (CV)0.4994031
Kurtosis-1.2730307
Mean4.8554074 × 109
Median Absolute Deviation (MAD)2.418763 × 109
Skewness0.1771249
Sum4.5640829 × 1012
Variance5.8796816 × 1018
MonotonicityIncreasing
2023-01-21T14:25:15.101764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1503960366 31
 
3.3%
4319703577 31
 
3.3%
8583815059 31
 
3.3%
8378563200 31
 
3.3%
8053475328 31
 
3.3%
7086361926 31
 
3.3%
6962181067 31
 
3.3%
5553957443 31
 
3.3%
4702921684 31
 
3.3%
4558609924 31
 
3.3%
Other values (23) 630
67.0%
ValueCountFrequency (%)
1503960366 31
3.3%
1624580081 31
3.3%
1644430081 30
3.2%
1844505072 31
3.3%
1927972279 31
3.3%
2022484408 31
3.3%
2026352035 31
3.3%
2320127002 31
3.3%
2347167796 18
1.9%
2873212765 31
3.3%
ValueCountFrequency (%)
8877689391 31
3.3%
8792009665 29
3.1%
8583815059 31
3.3%
8378563200 31
3.3%
8253242879 19
2.0%
8053475328 31
3.3%
7086361926 31
3.3%
7007744171 26
2.8%
6962181067 31
3.3%
6775888955 26
2.8%

ActivityDate
Categorical

Distinct31
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size14.7 KiB
4/12/2016
 
33
4/14/2016
 
33
4/15/2016
 
33
4/13/2016
 
33
4/23/2016
 
32
Other values (26)
776 

Length

Max length9
Median length9
Mean length8.7255319
Min length8

Characters and Unicode

Total characters8202
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4/12/2016
2nd row4/13/2016
3rd row4/14/2016
4th row4/15/2016
5th row4/16/2016

Common Values

ValueCountFrequency (%)
4/12/2016 33
 
3.5%
4/14/2016 33
 
3.5%
4/15/2016 33
 
3.5%
4/13/2016 33
 
3.5%
4/23/2016 32
 
3.4%
4/29/2016 32
 
3.4%
4/28/2016 32
 
3.4%
4/26/2016 32
 
3.4%
4/25/2016 32
 
3.4%
4/24/2016 32
 
3.4%
Other values (21) 616
65.5%

Length

2023-01-21T14:25:15.169278image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4/12/2016 33
 
3.5%
4/15/2016 33
 
3.5%
4/13/2016 33
 
3.5%
4/14/2016 33
 
3.5%
4/22/2016 32
 
3.4%
4/21/2016 32
 
3.4%
4/16/2016 32
 
3.4%
4/18/2016 32
 
3.4%
4/19/2016 32
 
3.4%
4/20/2016 32
 
3.4%
Other values (21) 616
65.5%

Most occurring characters

ValueCountFrequency (%)
/ 1880
22.9%
2 1375
16.8%
1 1357
16.5%
6 1033
12.6%
0 1029
12.5%
4 705
 
8.6%
5 423
 
5.2%
3 125
 
1.5%
7 93
 
1.1%
9 91
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6322
77.1%
Other Punctuation 1880
 
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1375
21.7%
1 1357
21.5%
6 1033
16.3%
0 1029
16.3%
4 705
11.2%
5 423
 
6.7%
3 125
 
2.0%
7 93
 
1.5%
9 91
 
1.4%
8 91
 
1.4%
Other Punctuation
ValueCountFrequency (%)
/ 1880
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 1880
22.9%
2 1375
16.8%
1 1357
16.5%
6 1033
12.6%
0 1029
12.5%
4 705
 
8.6%
5 423
 
5.2%
3 125
 
1.5%
7 93
 
1.1%
9 91
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 1880
22.9%
2 1375
16.8%
1 1357
16.5%
6 1033
12.6%
0 1029
12.5%
4 705
 
8.6%
5 423
 
5.2%
3 125
 
1.5%
7 93
 
1.1%
9 91
 
1.1%

TotalSteps
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct842
Distinct (%)89.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7637.9106
Minimum0
Maximum36019
Zeros77
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.233852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13789.75
median7405.5
Q310727
95-th percentile15485.1
Maximum36019
Range36019
Interquartile range (IQR)6937.25

Descriptive statistics

Standard deviation5087.1507
Coefficient of variation (CV)0.66603957
Kurtosis1.1691112
Mean7637.9106
Median Absolute Deviation (MAD)3446.5
Skewness0.65289494
Sum7179636
Variance25879103
MonotonicityNot monotonic
2023-01-21T14:25:15.305997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 77
 
8.2%
244 2
 
0.2%
6708 2
 
0.2%
9167 2
 
0.2%
6175 2
 
0.2%
10538 2
 
0.2%
1510 2
 
0.2%
8538 2
 
0.2%
7937 2
 
0.2%
4363 2
 
0.2%
Other values (832) 845
89.9%
ValueCountFrequency (%)
0 77
8.2%
4 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
16 1
 
0.1%
17 1
 
0.1%
29 1
 
0.1%
31 1
 
0.1%
42 1
 
0.1%
44 1
 
0.1%
ValueCountFrequency (%)
36019 1
0.1%
29326 1
0.1%
27745 1
0.1%
23629 1
0.1%
23186 1
0.1%
22988 1
0.1%
22770 1
0.1%
22359 1
0.1%
22244 1
0.1%
22026 1
0.1%

TotalDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct615
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4897021
Minimum0
Maximum28.030001
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.381451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6199999
median5.2449999
Q37.7125
95-th percentile11.6565
Maximum28.030001
Range28.030001
Interquartile range (IQR)5.0925001

Descriptive statistics

Standard deviation3.9246059
Coefficient of variation (CV)0.71490325
Kurtosis3.1130184
Mean5.4897021
Median Absolute Deviation (MAD)2.5600001
Skewness1.1262736
Sum5160.32
Variance15.402532
MonotonicityNot monotonic
2023-01-21T14:25:15.449464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.599999905 5
 
0.5%
0.009999999776 5
 
0.5%
3.910000086 4
 
0.4%
4.949999809 4
 
0.4%
1.789999962 4
 
0.4%
4.329999924 4
 
0.4%
2.680000067 4
 
0.4%
3.50999999 4
 
0.4%
4.900000095 4
 
0.4%
Other values (605) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 2
 
0.2%
0.03999999911 1
 
0.1%
0.07999999821 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 1
 
0.1%
ValueCountFrequency (%)
28.03000069 1
0.1%
26.71999931 1
0.1%
25.29000092 1
0.1%
20.64999962 1
0.1%
20.39999962 1
0.1%
19.55999947 1
0.1%
19.34000015 1
0.1%
18.97999954 1
0.1%
18.25 1
0.1%
18.11000061 1
0.1%

TrackerDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct613
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4753511
Minimum0
Maximum28.030001
Zeros78
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.521614image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.6199999
median5.2449999
Q37.71
95-th percentile11.6565
Maximum28.030001
Range28.030001
Interquartile range (IQR)5.0900002

Descriptive statistics

Standard deviation3.9072759
Coefficient of variation (CV)0.71361195
Kurtosis3.2038891
Mean5.4753511
Median Absolute Deviation (MAD)2.5550003
Skewness1.1345496
Sum5146.83
Variance15.266805
MonotonicityNot monotonic
2023-01-21T14:25:15.590094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
8.3%
2.599999905 5
 
0.5%
0.009999999776 5
 
0.5%
3.910000086 4
 
0.4%
2.680000067 4
 
0.4%
1.789999962 4
 
0.4%
4.329999924 4
 
0.4%
4.949999809 4
 
0.4%
3.50999999 4
 
0.4%
8.739999771 4
 
0.4%
Other values (603) 824
87.7%
ValueCountFrequency (%)
0 78
8.3%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 2
 
0.2%
0.03999999911 1
 
0.1%
0.07999999821 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 1
 
0.1%
ValueCountFrequency (%)
28.03000069 1
0.1%
26.71999931 1
0.1%
25.29000092 1
0.1%
20.64999962 1
0.1%
20.39999962 1
0.1%
19.55999947 1
0.1%
19.34000015 1
0.1%
18.97999954 1
0.1%
18.25 1
0.1%
18.11000061 1
0.1%

LoggedActivitiesDistance
Real number (ℝ)

Distinct19
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10817094
Minimum0
Maximum4.942142
Zeros908
Zeros (%)96.6%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.651774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4.942142
Range4.942142
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.61989652
Coefficient of variation (CV)5.7307121
Kurtosis41.295941
Mean0.10817094
Median Absolute Deviation (MAD)0
Skewness6.2974404
Sum101.68068
Variance0.38427169
MonotonicityNot monotonic
2023-01-21T14:25:15.707045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 908
96.6%
2.092147112 9
 
1.0%
2.253081083 7
 
0.7%
4.081692219 1
 
0.1%
4.861792088 1
 
0.1%
4.878232002 1
 
0.1%
4.912367821 1
 
0.1%
2.832325935 1
 
0.1%
4.911146164 1
 
0.1%
4.885604858 1
 
0.1%
Other values (9) 9
 
1.0%
ValueCountFrequency (%)
0 908
96.6%
1.959596038 1
 
0.1%
2.092147112 9
 
1.0%
2.253081083 7
 
0.7%
2.785175085 1
 
0.1%
2.832325935 1
 
0.1%
3.167821884 1
 
0.1%
3.285414934 1
 
0.1%
4.081692219 1
 
0.1%
4.851306915 1
 
0.1%
ValueCountFrequency (%)
4.94214201 1
0.1%
4.930550098 1
0.1%
4.924840927 1
0.1%
4.912367821 1
0.1%
4.911146164 1
0.1%
4.885604858 1
0.1%
4.878232002 1
0.1%
4.869782925 1
0.1%
4.861792088 1
0.1%
4.851306915 1
0.1%

VeryActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct333
Distinct (%)35.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5026809
Minimum0
Maximum21.92
Zeros413
Zeros (%)43.9%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.772760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.20999999
Q32.0524999
95-th percentile6.4030001
Maximum21.92
Range21.92
Interquartile range (IQR)2.0524999

Descriptive statistics

Standard deviation2.6589412
Coefficient of variation (CV)1.769465
Kurtosis11.910951
Mean1.5026809
Median Absolute Deviation (MAD)0.20999999
Skewness2.99617
Sum1412.52
Variance7.0699681
MonotonicityNot monotonic
2023-01-21T14:25:15.842379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 413
43.9%
0.0700000003 9
 
1.0%
0.05999999866 6
 
0.6%
0.1400000006 5
 
0.5%
0.3300000131 5
 
0.5%
0.3400000036 4
 
0.4%
1.059999943 4
 
0.4%
0.3600000143 4
 
0.4%
1.00999999 4
 
0.4%
2.789999962 4
 
0.4%
Other values (323) 482
51.3%
ValueCountFrequency (%)
0 413
43.9%
0.01999999955 2
 
0.2%
0.03999999911 1
 
0.1%
0.05000000075 3
 
0.3%
0.05999999866 6
 
0.6%
0.0700000003 9
 
1.0%
0.07999999821 4
 
0.4%
0.09000000358 1
 
0.1%
0.1099999994 3
 
0.3%
0.1199999973 3
 
0.3%
ValueCountFrequency (%)
21.92000008 1
0.1%
21.65999985 1
0.1%
13.39999962 1
0.1%
13.26000023 1
0.1%
13.23999977 1
0.1%
13.22000027 1
0.1%
13.13000011 1
0.1%
13.06999969 1
0.1%
12.78999996 1
0.1%
12.53999996 1
0.1%

ModeratelyActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct211
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56754255
Minimum0
Maximum6.48
Zeros386
Zeros (%)41.1%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:15.916994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.23999999
Q30.80000001
95-th percentile2.1300001
Maximum6.48
Range6.48
Interquartile range (IQR)0.80000001

Descriptive statistics

Standard deviation0.88358032
Coefficient of variation (CV)1.556853
Kurtosis10.125629
Mean0.56754255
Median Absolute Deviation (MAD)0.23999999
Skewness2.7711936
Sum533.49
Variance0.78071418
MonotonicityNot monotonic
2023-01-21T14:25:15.987447image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 386
41.1%
0.200000003 9
 
1.0%
0.2800000012 9
 
1.0%
0.400000006 9
 
1.0%
0.25 8
 
0.9%
0.3100000024 8
 
0.9%
0.9300000072 8
 
0.9%
0.4199999869 8
 
0.9%
0.2700000107 7
 
0.7%
0.5699999928 7
 
0.7%
Other values (201) 481
51.2%
ValueCountFrequency (%)
0 386
41.1%
0.009999999776 1
 
0.1%
0.01999999955 1
 
0.1%
0.02999999933 3
 
0.3%
0.03999999911 3
 
0.3%
0.05000000075 3
 
0.3%
0.05999999866 3
 
0.3%
0.0700000003 2
 
0.2%
0.07999999821 4
 
0.4%
0.09000000358 2
 
0.2%
ValueCountFrequency (%)
6.480000019 1
 
0.1%
6.210000038 1
 
0.1%
5.599999905 1
 
0.1%
5.400000095 1
 
0.1%
5.239999771 1
 
0.1%
5.119999886 1
 
0.1%
4.579999924 1
 
0.1%
4.559999943 1
 
0.1%
4.349999905 1
 
0.1%
4.21999979 3
0.3%

LightActiveDistance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct491
Distinct (%)52.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3408191
Minimum0
Maximum10.71
Zeros85
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.187105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.945
median3.3649999
Q34.7825001
95-th percentile6.462
Maximum10.71
Range10.71
Interquartile range (IQR)2.8375001

Descriptive statistics

Standard deviation2.0406554
Coefficient of variation (CV)0.61082486
Kurtosis-0.18030027
Mean3.3408191
Median Absolute Deviation (MAD)1.4200002
Skewness0.18224747
Sum3140.37
Variance4.1642744
MonotonicityNot monotonic
2023-01-21T14:25:16.272792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 85
 
9.0%
4.179999828 6
 
0.6%
3.170000076 6
 
0.6%
4.880000114 6
 
0.6%
3.230000019 6
 
0.6%
3.940000057 5
 
0.5%
3.25999999 5
 
0.5%
0.009999999776 5
 
0.5%
4.460000038 5
 
0.5%
5.409999847 5
 
0.5%
Other values (481) 806
85.7%
ValueCountFrequency (%)
0 85
9.0%
0.009999999776 5
 
0.5%
0.01999999955 1
 
0.1%
0.02999999933 3
 
0.3%
0.03999999911 1
 
0.1%
0.05999999866 1
 
0.1%
0.09000000358 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
0.1299999952 2
 
0.2%
ValueCountFrequency (%)
10.71000004 1
0.1%
10.56999969 1
0.1%
10.30000019 1
0.1%
9.479999542 1
0.1%
9.460000038 1
0.1%
8.970000267 1
0.1%
8.789999962 1
0.1%
8.680000305 1
0.1%
8.409999847 1
0.1%
8.270000458 1
0.1%

SedentaryActiveDistance
Real number (ℝ)

Distinct9
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.001606383
Minimum0
Maximum0.11
Zeros858
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.342098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.0099999998
Maximum0.11
Range0.11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0073461763
Coefficient of variation (CV)4.5731164
Kurtosis99.127446
Mean0.001606383
Median Absolute Deviation (MAD)0
Skewness8.5898992
Sum1.51
Variance5.3966306 × 10-5
MonotonicityNot monotonic
2023-01-21T14:25:16.400125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 858
91.3%
0.009999999776 50
 
5.3%
0.01999999955 21
 
2.2%
0.02999999933 4
 
0.4%
0.05000000075 3
 
0.3%
0.0700000003 1
 
0.1%
0.03999999911 1
 
0.1%
0.1099999994 1
 
0.1%
0.1000000015 1
 
0.1%
ValueCountFrequency (%)
0 858
91.3%
0.009999999776 50
 
5.3%
0.01999999955 21
 
2.2%
0.02999999933 4
 
0.4%
0.03999999911 1
 
0.1%
0.05000000075 3
 
0.3%
0.0700000003 1
 
0.1%
0.1000000015 1
 
0.1%
0.1099999994 1
 
0.1%
ValueCountFrequency (%)
0.1099999994 1
 
0.1%
0.1000000015 1
 
0.1%
0.0700000003 1
 
0.1%
0.05000000075 3
 
0.3%
0.03999999911 1
 
0.1%
0.02999999933 4
 
0.4%
0.01999999955 21
 
2.2%
0.009999999776 50
 
5.3%
0 858
91.3%

VeryActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct122
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.164894
Minimum0
Maximum210
Zeros409
Zeros (%)43.5%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.473957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q332
95-th percentile93.05
Maximum210
Range210
Interquartile range (IQR)32

Descriptive statistics

Standard deviation32.844803
Coefficient of variation (CV)1.551853
Kurtosis5.7780701
Mean21.164894
Median Absolute Deviation (MAD)4
Skewness2.1761432
Sum19895
Variance1078.7811
MonotonicityNot monotonic
2023-01-21T14:25:16.551040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
8 15
 
1.6%
6 14
 
1.5%
11 14
 
1.5%
19 13
 
1.4%
5 13
 
1.4%
14 12
 
1.3%
Other values (112) 393
41.8%
ValueCountFrequency (%)
0 409
43.5%
1 23
 
2.4%
2 18
 
1.9%
3 16
 
1.7%
4 10
 
1.1%
5 13
 
1.4%
6 14
 
1.5%
7 11
 
1.2%
8 15
 
1.6%
9 7
 
0.7%
ValueCountFrequency (%)
210 1
0.1%
207 1
0.1%
200 1
0.1%
194 1
0.1%
186 1
0.1%
184 1
0.1%
137 1
0.1%
132 1
0.1%
129 1
0.1%
125 2
0.2%

FairlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.564894
Minimum0
Maximum143
Zeros384
Zeros (%)40.9%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.630034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q319
95-th percentile51
Maximum143
Range143
Interquartile range (IQR)19

Descriptive statistics

Standard deviation19.987404
Coefficient of variation (CV)1.4734656
Kurtosis7.9957314
Mean13.564894
Median Absolute Deviation (MAD)6
Skewness2.479492
Sum12751
Variance399.49632
MonotonicityNot monotonic
2023-01-21T14:25:16.711373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 384
40.9%
8 36
 
3.8%
6 23
 
2.4%
5 23
 
2.4%
16 22
 
2.3%
7 20
 
2.1%
10 19
 
2.0%
9 19
 
2.0%
13 18
 
1.9%
11 18
 
1.9%
Other values (71) 358
38.1%
ValueCountFrequency (%)
0 384
40.9%
1 10
 
1.1%
2 8
 
0.9%
3 9
 
1.0%
4 14
 
1.5%
5 23
 
2.4%
6 23
 
2.4%
7 20
 
2.1%
8 36
 
3.8%
9 19
 
2.0%
ValueCountFrequency (%)
143 1
 
0.1%
125 1
 
0.1%
122 1
 
0.1%
116 1
 
0.1%
115 1
 
0.1%
113 1
 
0.1%
98 1
 
0.1%
96 1
 
0.1%
95 5
0.5%
94 1
 
0.1%

LightlyActiveMinutes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct335
Distinct (%)35.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192.81277
Minimum0
Maximum518
Zeros84
Zeros (%)8.9%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.794416image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1127
median199
Q3264
95-th percentile369.05
Maximum518
Range518
Interquartile range (IQR)137

Descriptive statistics

Standard deviation109.1747
Coefficient of variation (CV)0.56622132
Kurtosis-0.36011793
Mean192.81277
Median Absolute Deviation (MAD)69
Skewness-0.037929343
Sum181244
Variance11919.115
MonotonicityNot monotonic
2023-01-21T14:25:16.873014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 84
 
8.9%
206 12
 
1.3%
258 10
 
1.1%
195 9
 
1.0%
214 8
 
0.9%
139 7
 
0.7%
238 7
 
0.7%
141 7
 
0.7%
199 7
 
0.7%
227 7
 
0.7%
Other values (325) 782
83.2%
ValueCountFrequency (%)
0 84
8.9%
1 3
 
0.3%
2 4
 
0.4%
3 3
 
0.3%
4 1
 
0.1%
9 3
 
0.3%
10 2
 
0.2%
11 1
 
0.1%
12 2
 
0.2%
15 1
 
0.1%
ValueCountFrequency (%)
518 1
0.1%
513 1
0.1%
512 1
0.1%
487 1
0.1%
480 1
0.1%
475 1
0.1%
461 1
0.1%
458 1
0.1%
448 1
0.1%
439 1
0.1%

SedentaryMinutes
Real number (ℝ)

Distinct549
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean991.21064
Minimum0
Maximum1440
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:16.949517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile536.7
Q1729.75
median1057.5
Q31229.5
95-th percentile1440
Maximum1440
Range1440
Interquartile range (IQR)499.75

Descriptive statistics

Standard deviation301.26744
Coefficient of variation (CV)0.30393887
Kurtosis-0.66595003
Mean991.21064
Median Absolute Deviation (MAD)261
Skewness-0.29449809
Sum931738
Variance90762.068
MonotonicityNot monotonic
2023-01-21T14:25:17.023544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1440 79
 
8.4%
1182 7
 
0.7%
692 6
 
0.6%
1112 5
 
0.5%
1131 5
 
0.5%
1122 5
 
0.5%
1105 5
 
0.5%
709 5
 
0.5%
1119 5
 
0.5%
728 5
 
0.5%
Other values (539) 813
86.5%
ValueCountFrequency (%)
0 1
0.1%
2 1
0.1%
13 1
0.1%
48 1
0.1%
111 1
0.1%
125 1
0.1%
127 1
0.1%
218 1
0.1%
222 1
0.1%
241 1
0.1%
ValueCountFrequency (%)
1440 79
8.4%
1439 3
 
0.3%
1438 3
 
0.3%
1437 2
 
0.2%
1431 1
 
0.1%
1430 2
 
0.2%
1428 1
 
0.1%
1423 1
 
0.1%
1420 1
 
0.1%
1413 1
 
0.1%

Calories
Real number (ℝ)

Distinct734
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2303.6096
Minimum0
Maximum4900
Zeros4
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size14.7 KiB
2023-01-21T14:25:17.102672image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1372.85
Q11828.5
median2134
Q32793.25
95-th percentile3654.25
Maximum4900
Range4900
Interquartile range (IQR)964.75

Descriptive statistics

Standard deviation718.16686
Coefficient of variation (CV)0.3117572
Kurtosis0.62502694
Mean2303.6096
Median Absolute Deviation (MAD)467
Skewness0.42245048
Sum2165393
Variance515763.64
MonotonicityNot monotonic
2023-01-21T14:25:17.180549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1980 13
 
1.4%
2063 11
 
1.2%
1841 9
 
1.0%
1688 9
 
1.0%
1347 8
 
0.9%
2225 4
 
0.4%
1819 4
 
0.4%
2044 4
 
0.4%
1922 4
 
0.4%
0 4
 
0.4%
Other values (724) 870
92.6%
ValueCountFrequency (%)
0 4
0.4%
52 1
 
0.1%
57 1
 
0.1%
120 1
 
0.1%
257 1
 
0.1%
403 1
 
0.1%
665 1
 
0.1%
741 1
 
0.1%
928 1
 
0.1%
1002 1
 
0.1%
ValueCountFrequency (%)
4900 1
0.1%
4552 1
0.1%
4547 1
0.1%
4546 1
0.1%
4501 1
0.1%
4398 1
0.1%
4392 1
0.1%
4274 1
0.1%
4236 1
0.1%
4163 1
0.1%

Date
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

WeightKg
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

WeightPounds
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

Fat
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

BMI
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

IsManualReport
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

LogId
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing940
Missing (%)100.0%
Memory size14.7 KiB

Interactions

2023-01-21T14:25:13.671788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:00.636399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.786146image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.772901image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.816544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.751284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.697590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.715687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.675446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.704420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.663002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.685710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.658232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.696320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.740771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:00.721369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.860148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.844945image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.884896image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.821627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.765940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.786225image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.745541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.772855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.731176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.758165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.727793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.767318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.809962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:00.796190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.933088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.915730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.954242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.891726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.931968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.858160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.816826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.845101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.802285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.830095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.797674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.841754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.874613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:00.867658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.003101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.983661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.018869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.958453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.995185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.926079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.880701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.913568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.867192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.897938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.862580image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.910984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.940586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:00.937119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.071404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.050811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.085560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.025364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.059253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.992959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.945854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.980105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.930339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.968025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.930516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.979966image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.005581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.007120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.140540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.117936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.150479image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.090859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.124662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.061589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.106853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.046294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.995056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.035819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.997550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.048715image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.069524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.076052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.205931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.183697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.215679image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.155714image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.188087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.127046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.170368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.111644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.058636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.101645image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.063271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.115659image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.136299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.148079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.277437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.252012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.282642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.224432image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.255794image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.195467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.238127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.179652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.127318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.171300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.131150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.185332image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.201492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.217556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.348001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.318035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.349419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.292283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.321546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.264845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.303206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.248876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.291097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.239127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.196764image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.253263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.269936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.288985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.419475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.385975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.416175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.358989image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.388073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.332910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.370113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.318284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.356913image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.310312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.264365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.324885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.333192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.356801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.486881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.449618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.479946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.425311image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.450477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.399070image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.433951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.383678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.421452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.375837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.422549image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.390707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.402356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.580625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.561210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.520783image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.548920image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.494782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.517584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.470045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.503098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.455866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.489597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.448308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.492367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.463999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.562367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.649261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.631775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.588796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.616099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.562460image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.583288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.537251image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.569396image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.522377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.553855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.517508image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.557777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.532698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:14.631366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:01.719415image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:02.703991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:03.752630image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:04.684728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:05.631782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:06.651532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:07.609289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:08.639120image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:09.597292image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:10.621831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:11.588467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:12.628656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-01-21T14:25:13.603881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-01-21T14:25:17.249114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
IdTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesActivityDate
Id1.0000.1580.1990.1970.2100.2230.1110.030-0.1140.2510.125-0.084-0.0640.4290.000
TotalSteps0.1581.0000.9920.9920.1800.7700.7040.7150.0150.7490.6890.581-0.4280.5590.000
TotalDistance0.1990.9921.0001.0000.2030.7760.7010.7150.0130.7520.6850.559-0.4140.6170.000
TrackerDistance0.1970.9921.0001.0000.1930.7750.7010.7140.0110.7510.6860.558-0.4150.6170.000
LoggedActivitiesDistance0.2100.1800.2030.1931.0000.2260.1570.1390.0100.2650.1330.057-0.0870.2260.000
VeryActiveDistance0.2230.7700.7760.7750.2261.0000.7490.285-0.0640.9700.7430.158-0.2350.4970.000
ModeratelyActiveDistance0.1110.7040.7010.7010.1570.7491.0000.361-0.0960.7340.9800.244-0.3080.4030.016
LightActiveDistance0.0300.7150.7150.7140.1390.2850.3611.0000.1420.2850.3450.878-0.4660.4650.000
SedentaryActiveDistance-0.1140.0150.0130.0110.010-0.064-0.0960.1421.000-0.057-0.1030.1940.0960.0100.000
VeryActiveMinutes0.2510.7490.7520.7510.2650.9700.7340.285-0.0571.0000.7460.152-0.2410.5400.000
FairlyActiveMinutes0.1250.6890.6850.6860.1330.7430.9800.345-0.1030.7461.0000.232-0.3140.4350.051
LightlyActiveMinutes-0.0840.5810.5590.5580.0570.1580.2440.8780.1940.1520.2321.000-0.4800.2860.000
SedentaryMinutes-0.064-0.428-0.414-0.415-0.087-0.235-0.308-0.4660.096-0.241-0.314-0.4801.000-0.1520.097
Calories0.4290.5590.6170.6170.2260.4970.4030.4650.0100.5400.4350.286-0.1521.0000.119
ActivityDate0.0000.0000.0000.0000.0000.0000.0160.0000.0000.0000.0510.0000.0970.1191.000

Missing values

2023-01-21T14:25:14.738483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-21T14:25:14.914987image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesDateWeightKgWeightPoundsFatBMIIsManualReportLogId
015039603664/12/2016131628.508.500.01.880.556.060.025133287281985NaNNaNNaNNaNNaNNaNNaN
115039603664/13/2016107356.976.970.01.570.694.710.021192177761797NaNNaNNaNNaNNaNNaNNaN
215039603664/14/2016104606.746.740.02.440.403.910.0301118112181776NaNNaNNaNNaNNaNNaNNaN
315039603664/15/201697626.286.280.02.141.262.830.029342097261745NaNNaNNaNNaNNaNNaNNaN
415039603664/16/2016126698.168.160.02.710.415.040.036102217731863NaNNaNNaNNaNNaNNaNNaN
515039603664/17/201697056.486.480.03.190.782.510.038201645391728NaNNaNNaNNaNNaNNaNNaN
615039603664/18/2016130198.598.590.03.250.644.710.0421623311491921NaNNaNNaNNaNNaNNaNNaN
715039603664/19/2016155069.889.880.03.531.325.030.050312647752035NaNNaNNaNNaNNaNNaNNaN
815039603664/20/2016105446.686.680.01.960.484.240.028122058181786NaNNaNNaNNaNNaNNaNNaN
915039603664/21/201698196.346.340.01.340.354.650.01982118381775NaNNaNNaNNaNNaNNaNNaN
IdActivityDateTotalStepsTotalDistanceTrackerDistanceLoggedActivitiesDistanceVeryActiveDistanceModeratelyActiveDistanceLightActiveDistanceSedentaryActiveDistanceVeryActiveMinutesFairlyActiveMinutesLightlyActiveMinutesSedentaryMinutesCaloriesDateWeightKgWeightPoundsFatBMIIsManualReportLogId
93088776893915/3/2016108188.2100008.2100000.01.390.106.670.0119322911892817NaNNaNNaNNaNNaNNaNNaN
93188776893915/4/20161819316.29999916.2999990.010.420.315.530.0066821211543477NaNNaNNaNNaNNaNNaNNaN
93288776893915/5/20161405510.67000010.6700000.05.460.824.370.00671518811703052NaNNaNNaNNaNNaNNaNNaN
93388776893915/6/20162172719.34000019.3400000.012.790.296.160.00961723210954015NaNNaNNaNNaNNaNNaNNaN
93488776893915/7/2016123328.1300008.1300000.00.080.966.990.001052827110364142NaNNaNNaNNaNNaNNaNNaN
93588776893915/8/2016106868.1100008.1100000.01.080.206.800.0017424511742847NaNNaNNaNNaNNaNNaNNaN
93688776893915/9/20162022618.25000018.2500000.011.100.806.240.05731921711313710NaNNaNNaNNaNNaNNaNNaN
93788776893915/10/2016107338.1500008.1500000.01.350.466.280.00181122411872832NaNNaNNaNNaNNaNNaNNaN
93888776893915/11/20162142019.55999919.5599990.013.220.415.890.00881221311273832NaNNaNNaNNaNNaNNaNNaN
93988776893915/12/201680646.1200006.1200000.01.820.044.250.002311377701849NaNNaNNaNNaNNaNNaNNaN